Sufiyan Shaikh

Designed and implemented a Recommendation System using collaborative and content-based filtering techniques to deliver personalized suggestions based on user behavior and item similarity. Built a Cuisine Classification Model to categorize restaurants based on menu and metadata features, applying data preprocessing, feature engineering, and supervised learning techniques. Developed a Restaurant Rating Prediction Model to estimate customer ratings using regression algorithms, focusing on performance optimization and interpretability. Structured projects using a modular, scalable codebase with separate pipelines for data ingestion, preprocessing, training, and evaluation. Performed data cleaning, EDA, feature selection, and model tuning to improve accuracy and generalization. Implemented model evaluation metrics and error analysis to validate performance across multiple scenarios. Debugged pipeline failures, handled data inconsistencies, and resolved environment and dependency issues during development. Followed industry-standard ML workflows, including version control, reproducibility, and clear documentation. Deployed models locally using Flask APIs to simulate real production environments.